






"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch






class 	a		:






    def __init__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Dict=14			,		__SCREAMING_SNAKE_CASE	:				Any=7			,		__SCREAMING_SNAKE_CASE	:				Any=True			,		__SCREAMING_SNAKE_CASE	:				Any=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=False			,		__SCREAMING_SNAKE_CASE	:				Dict=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=99			,		__SCREAMING_SNAKE_CASE	:				Tuple=32			,		__SCREAMING_SNAKE_CASE	:				str=4			,		__SCREAMING_SNAKE_CASE	:				List[str]=4			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=4			,		__SCREAMING_SNAKE_CASE	:				List[Any]=37			,		__SCREAMING_SNAKE_CASE	:				Tuple="gelu"			,		__SCREAMING_SNAKE_CASE	:				Dict=0.1			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				List[Any]=512			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		)	->						Optional[int]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		seq_length
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_input_mask
      lowerCamelCase_       	=		use_token_type_ids
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		vocab_size
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		rotary_dim
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		max_position_embeddings
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		None
      lowerCamelCase_       	=		vocab_size - 1
      lowerCamelCase_       	=		vocab_size - 1
      lowerCamelCase_       	=		vocab_size - 1






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size   )

      lowerCamelCase_       	=		None
      if self.use_input_mask:
        lowerCamelCase_       	=		random_attention_mask([self.batch_size, self.seq_length]   )

      lowerCamelCase_       	=		GPTJConfig(
          vocab_size=self.vocab_size			,		n_embd=self.hidden_size			,		n_layer=self.num_hidden_layers			,		n_head=self.num_attention_heads			,		n_positions=self.max_position_embeddings			,		use_cache=SCREAMING_SNAKE_CASE_			,		bos_token_id=self.bos_token_id			,		eos_token_id=self.eos_token_id			,		pad_token_id=self.pad_token_id			,		rotary_dim=self.rotary_dim			,		)

      return (config, input_ids, input_mask)






    def   UpperCamelCase			(			self	:				str   )	->						Union[str, Any]:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Tuple   )	->						Dict:
      lowerCamelCase_       	=		20
      lowerCamelCase_       	=		model_class_name(SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		model.init_cache(input_ids.shape[0]			,		SCREAMING_SNAKE_CASE_   )
      lowerCamelCase_       	=		jnp.ones((input_ids.shape[0], max_decoder_length)			,		dtype='i4'   )

      lowerCamelCase_       	=		jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1   )[None, :]			,		(input_ids.shape[0], input_ids.shape[-1] - 1)   )
      lowerCamelCase_       	=		model(
          input_ids[:, :-1]			,		attention_mask=SCREAMING_SNAKE_CASE_			,		past_key_values=SCREAMING_SNAKE_CASE_			,		position_ids=SCREAMING_SNAKE_CASE_			,		)

      lowerCamelCase_       	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,		dtype='i4'   )
      lowerCamelCase_       	=		model(
          input_ids[:, -1:]			,		attention_mask=SCREAMING_SNAKE_CASE_			,		past_key_values=outputs_cache.past_key_values			,		position_ids=SCREAMING_SNAKE_CASE_			,		)

      lowerCamelCase_       	=		model(SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
      self.parent.assertTrue(diff < 1e-3			,		msg=F'''Max diff is {diff}'''   )






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Dict:
      lowerCamelCase_       	=		20
      lowerCamelCase_       	=		model_class_name(SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]			,		axis=-1			,		)

      lowerCamelCase_       	=		model.init_cache(input_ids.shape[0]			,		SCREAMING_SNAKE_CASE_   )
      lowerCamelCase_       	=		jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1   )[None, :]			,		(input_ids.shape[0], input_ids.shape[-1] - 1)   )

      lowerCamelCase_       	=		model(
          input_ids[:, :-1]			,		attention_mask=SCREAMING_SNAKE_CASE_			,		past_key_values=SCREAMING_SNAKE_CASE_			,		position_ids=SCREAMING_SNAKE_CASE_			,		)
      lowerCamelCase_       	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,		dtype='i4'   )
      lowerCamelCase_       	=		model(
          input_ids[:, -1:]			,		past_key_values=outputs_cache.past_key_values			,		attention_mask=SCREAMING_SNAKE_CASE_			,		position_ids=SCREAMING_SNAKE_CASE_			,		)

      lowerCamelCase_       	=		model(SCREAMING_SNAKE_CASE_			,		attention_mask=SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
      self.parent.assertTrue(diff < 1e-3			,		msg=F'''Max diff is {diff}'''   )






@require_flax
class 	a		(   lowercase__    , lowercase__    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE		:	Dict		=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def   UpperCamelCase			(			self	:				Any   )	->						Union[str, Any]:
      lowerCamelCase_       	=		FlaxGPTJModelTester(self   )






    def   UpperCamelCase			(			self	:				int   )	->						str:


      for model_class_name in self.all_model_classes:
        lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						Tuple:


      for model_class_name in self.all_model_classes:
        lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )






    @tooslow
    def   UpperCamelCase			(			self	:				Any   )	->						Dict:
      lowerCamelCase_       	=		GPTaTokenizer.from_pretrained('gpt2'			,		pad_token='<|endoftext|>'			,		padding_side='left'   )
      lowerCamelCase_       	=		tokenizer(['Hello this is a long string', 'Hey']			,		return_tensors='np'			,		padding=SCREAMING_SNAKE_CASE_			,		truncation=SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   )
      lowerCamelCase_       	=		False
      lowerCamelCase_       	=		model.config.eos_token_id

      lowerCamelCase_       	=		jax.jit(model.generate   )

      lowerCamelCase_       	=		jit_generate(
          inputs['input_ids']			,		attention_mask=inputs['attention_mask']			,		pad_token_id=tokenizer.pad_token_id   ).sequences

      lowerCamelCase_       	=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			,		skip_special_tokens=SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		[
          'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
          'Hey, I\'m a little late to the party. I\'m going to',
      ]

      self.assertListEqual(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )






    @is_pt_flax_cross_test
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						str:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
          # prepare inputs
          lowerCamelCase_       	=		self._prepare_for_class(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
          lowerCamelCase_       	=		{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          lowerCamelCase_       	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
          lowerCamelCase_       	=		getattr(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )

          lowerCamelCase_	,      lowerCamelCase_       	=		pt_inputs['input_ids'].shape
          lowerCamelCase_       	=		np.random.randint(0			,		seq_length - 1			,		size=(batch_size,)   )
          for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_   ):
            lowerCamelCase_       	=		0
            lowerCamelCase_       	=		1
            lowerCamelCase_       	=		0
            lowerCamelCase_       	=		1
          lowerCamelCase_       	=		pt_model_class(SCREAMING_SNAKE_CASE_   ).eval()
          lowerCamelCase_       	=		model_class(SCREAMING_SNAKE_CASE_			,		dtype=jnp.floataa   )

          lowerCamelCase_       	=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,		SCREAMING_SNAKE_CASE_   )
          lowerCamelCase_       	=		fx_state

          with torch.no_grad():
            lowerCamelCase_       	=		pt_model(**SCREAMING_SNAKE_CASE_   ).to_tuple()

          lowerCamelCase_       	=		fx_model(**SCREAMING_SNAKE_CASE_   ).to_tuple()
          self.assertEqual(len(SCREAMING_SNAKE_CASE_   )			,		len(SCREAMING_SNAKE_CASE_   )			,		'Output lengths differ between Flax and PyTorch'   )
          for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
            self.assert_almost_equals(fx_output[:, -1]			,		pt_output[:, -1].numpy()			,		4e-2   )

          with tempfile.TemporaryDirectory() as tmpdirname:
            pt_model.save_pretrained(SCREAMING_SNAKE_CASE_   )
            lowerCamelCase_       	=		model_class.from_pretrained(SCREAMING_SNAKE_CASE_			,		from_pt=SCREAMING_SNAKE_CASE_   )

          lowerCamelCase_       	=		fx_model_loaded(**SCREAMING_SNAKE_CASE_   ).to_tuple()
          self.assertEqual(
              len(SCREAMING_SNAKE_CASE_   )			,		len(SCREAMING_SNAKE_CASE_   )			,		'Output lengths differ between Flax and PyTorch'   )
          for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
            self.assert_almost_equals(fx_output_loaded[:, -1]			,		pt_output[:, -1].numpy()			,		4e-2   )






    @is_pt_flax_cross_test
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
          # prepare inputs
          lowerCamelCase_       	=		self._prepare_for_class(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
          lowerCamelCase_       	=		{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          lowerCamelCase_       	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
          lowerCamelCase_       	=		getattr(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )

          lowerCamelCase_       	=		pt_model_class(SCREAMING_SNAKE_CASE_   ).eval()
          lowerCamelCase_       	=		model_class(SCREAMING_SNAKE_CASE_			,		dtype=jnp.floataa   )

          lowerCamelCase_       	=		load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_			,		fx_model.params   )
          lowerCamelCase_	,      lowerCamelCase_       	=		pt_inputs['input_ids'].shape
          lowerCamelCase_       	=		np.random.randint(0			,		seq_length - 1			,		size=(batch_size,)   )
          for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_   ):
            lowerCamelCase_       	=		0
            lowerCamelCase_       	=		1
            lowerCamelCase_       	=		0
            lowerCamelCase_       	=		1

          # make sure weights are tied in PyTorch
          pt_model.tie_weights()

          with torch.no_grad():
            lowerCamelCase_       	=		pt_model(**SCREAMING_SNAKE_CASE_   ).to_tuple()

          lowerCamelCase_       	=		fx_model(**SCREAMING_SNAKE_CASE_   ).to_tuple()
          self.assertEqual(len(SCREAMING_SNAKE_CASE_   )			,		len(SCREAMING_SNAKE_CASE_   )			,		'Output lengths differ between Flax and PyTorch'   )
          for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
            self.assert_almost_equals(fx_output[:, -1]			,		pt_output[:, -1].numpy()			,		4e-2   )

          with tempfile.TemporaryDirectory() as tmpdirname:
            fx_model.save_pretrained(SCREAMING_SNAKE_CASE_   )
            lowerCamelCase_       	=		pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_			,		from_flax=SCREAMING_SNAKE_CASE_   )

          with torch.no_grad():
            lowerCamelCase_       	=		pt_model_loaded(**SCREAMING_SNAKE_CASE_   ).to_tuple()

          self.assertEqual(
              len(SCREAMING_SNAKE_CASE_   )			,		len(SCREAMING_SNAKE_CASE_   )			,		'Output lengths differ between Flax and PyTorch'   )
          for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
            self.assert_almost_equals(fx_output[:, -1]			,		pt_output[:, -1].numpy()			,		4e-2   )






    @tooslow
    def   UpperCamelCase			(			self	:				List[Any]   )	->						Union[str, Any]:


      for model_class_name in self.all_model_classes:
        lowerCamelCase_       	=		model_class_name.from_pretrained('EleutherAI/gpt-j-6B'   )
        lowerCamelCase_       	=		model(np.ones((1, 1)   )   )
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class 			lowercase					( lowercase__   ,lowercase__		):
			lowercase        =     '''nat'''

			lowercase        =     {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=4      ,SCREAMING_SNAKE_CASE_  :	Dict=3      ,SCREAMING_SNAKE_CASE_  :	int=64      ,SCREAMING_SNAKE_CASE_  :	Tuple=[3, 4, 6, 5]      ,SCREAMING_SNAKE_CASE_  :	Dict=[2, 4, 8, 16]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=7      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3.0      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=True      ,SCREAMING_SNAKE_CASE_  :	Any=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Tuple="gelu"      ,SCREAMING_SNAKE_CASE_  :	str=0.02      ,SCREAMING_SNAKE_CASE_  :	str=1e-5      ,SCREAMING_SNAKE_CASE_  :	int=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None      ,SCREAMING_SNAKE_CASE_  :	Tuple=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,)       ->	Tuple:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  patch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  embed_dim
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  num_heads
				lowerCAmelCase												=  kernel_size
				lowerCAmelCase												=  mlp_ratio
				lowerCAmelCase												=  qkv_bias
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  initializer_range
				# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				lowerCAmelCase												=  int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_			) - 1)			)
				lowerCAmelCase												=  layer_scale_init_value
				lowerCAmelCase												=  ['''stem'''] + [F"""stage{idx}""" for idx in range(1      ,len(SCREAMING_SNAKE_CASE_			) + 1			)]
				lowerCAmelCase							,       lowerCAmelCase												=  get_aligned_output_features_output_indices(
				    out_features=SCREAMING_SNAKE_CASE_      ,out_indices=SCREAMING_SNAKE_CASE_      ,stage_names=self.stage_names			)


